I am Joost (pronounced ‘Yoast’) van Amersfoort (which is a Dutch city) and I am currently pursuing a PhD at the Unversity of Oxford under supervision of Professor Yarin Gal (in OATML) and Professor Yee Whye Teh (in OxCSML).
I am interested in variational inference (for example done through the reparametrisation trick, dropout and reversible models) and its applications such as uncertainty estimation, active learning and generative modelling.
Previously, I was a research engineer at Twitter Cortex, where I worked on large scale video models and recommendation systems. I did my Master's at the University of Amsterdam, where I worked with Dr. Durk Kingma and Professor Max Welling. I've also done an internship at Facebook AI Research (FAIR) in Menlo Park during my Master's.
For a full overview see my Google Scholar page.
@article{alizadeh2022prosprect, title={Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients}, author={Alizadeh, Milad and Tailor, Shyam A. and Zintgaf, Luisa M and van Amersfoort, Joost and Farquhar, Sebastian and Lane, Nicholas Donald and Gal, Yarin}, booktitle={International Conference on Learning Representations (ICLR)}, year={2022} }
@article{jesson2021causal, title={Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data}, author={Jesson, Andrew and Panagiotis, Tigas and van Amersfoort, Joost and Kirsch, Andreas and Shalit, Uri and Gal, Yarin}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2021} }
@article{van2021on, title={On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty}, author={van Amersfoort, Joost and Smith, Lewis and Jesson, Andrew and Key, Oscar and Gal, Yarin}, journal={arXiv preprint arXiv:2102.11409}, year={2021} }
@article{smith2021can, title={Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective}, author={Smith, Lewis and van Amersfoort, Joost and Huang, Haiwen and Roberts, Stephen and Gal, Yarin}, journal={arXiv preprint arXiv:2106.02469}, year={2021} }
@article{mukhoti2021deterministic, title={Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty}, author={Mukhoti, Jishnu and Kirsch, Andreas and van Amersfoort, Joost and Torr, Philip HS and Gal, Yarin}, journal={arXiv preprint arXiv:2102.11582}, year={2021} }
@inproceedings{van2020uncertainty, title={Uncertainty Estimation Using a Single Deep Deterministic Neural Network}, author={van Amersfoort, Joost and Smith, Lewis and Teh, Yee Whye and Gal, Yarin}, booktitle={International Conference on Machine Learning (ICML)}, year={2020} }
@inproceedings{kirsch2019batchbald, title={BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning}, author={Kirsch, Andreas and van Amersfoort, Joost and Gal, Yarin}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2019} }
For a full overview of all my open source work, see my Github account.